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21.
Kazunori Nagasaka Takeshi Yoshida Yoshihiro Kita Fumio Hashimoto 《International Journal of Machine Tools and Manufacture》1987,27(2)
For optimization of abrasive cut-off operation, wheel wear equation must be identified before the operation is optimized. The equation is obtained by using GMDH algorithm with successive determination of trends containing interactive terms. In the model equation factors of grinding fluid are taken into consideration in addition to the factors of wheel, work material, feed (table speed) and wheel speed. For the identification of the model wheel wear tests are performed under the experimental design treating the above-mentioned factors as independent variables. The grinding ratio (output in the model) can be predicted for combinations of various factors using the model. With the wheel wear equation and machining cost model, the optimum combination of wheel, fluid, feed and wheel speed can be selected for a given work material. The relationships between these variables and the costs are investigated. 相似文献
22.
23.
Masahiro Nakagawa Tadashi Kondo Tsuyosi Kudo Shoichiro Takao Junji Ueno 《Artificial Life and Robotics》2009,14(2):118-122
In this study, we propose a revised radial basis function (RBF) neural network algorithm and apply this algorithm to computer-aided diagnosis (CAD) of the liver. First, the revised RBF neural network algorithm is applied to recognition of the liver regions, and the recognition results are compared with those obtained using the conventional RBF neural network and the conventional multilayered neural network trained using the back-propagation algorithm. It is shown that the revised RBF neural network is accurate, and is a useful method because the parameters are automatically determined. Then, the revised RBF neural network is applied to CAD of the liver cancer called hepatocellular carcinoma (HCC). 相似文献
24.
Kazuto Okazaki Tatsuya Ogiwara Dongshin Yang Kentaro Sakata Ken Saito Yoshifumi Sekine Fumio Uchikoba 《Artificial Life and Robotics》2011,16(2):229-233
This article presents the micro-electro-mechanical systems (MEMS) microrobot which demonstrates locomotion controlled by hardware
neural networks (HNN). The size of the microrobot fabricated by the MEMS technology is 4 × 4 × 3.5 mm. The frame of the robot
is made of silicon wafer, and it is equipped with a rotary-type actuator, a link mechanism, and six legs. The rotary-type
actuator generates rotational movement by applying an electrical current to artificial muscle wires. The locomotion of the
microrobot is obtained by the rotation of the rotary-type actuator. As in a living organism, the HNN realized robot control
without using any software programs, A/D converters, or additional driving circuits. A central pattern generator (CPG) model
was implemented as an HNN system to emulate the locomotion pattern. The MEMS microrobot emulated the locomotion method and
the neural networks of an insect with the rotary-type actuator, the link mechanism, and the HNN. The microrobot performed
forward and backward locomotion, and also changed direction by inputting an external trigger pulse. The locomotion speed was
0.325 mm/s and the step width was 1.3 mm. 相似文献
25.
26.
We solve the light transport problem by introducing a novel unbiased Monte Carlo algorithm called replica exchange light transport, inspired by the replica exchange Monte Carlo method in the fields of computational physics and statistical information processing. The replica exchange Monte Carlo method is a sampling technique whose operation resembles simulated annealing in optimization algorithms using a set of sampling distributions. We apply it to the solution of light transport integration by extending the probability density function of an integrand of the integration to a set of distributions. That set of distributions is composed of combinations of the path densities of different path generation types: uniform distributions in the integral domain, explicit and implicit paths in light (particle/photon) tracing, indirect paths in bidirectional path tracing, explicit and implicit paths in path tracing, and implicit caustics paths seen through specular surfaces including the delta function in path tracing. The replica‐exchange light transport algorithm generates a sequence of path samples from each distribution and samples the simultaneous distribution of those distributions as a stationary distribution by using the Markov chain Monte Carlo method. Then the algorithm combines the obtained path samples from each distribution using multiple importance sampling. We compare the images generated with our algorithm to those generated with bidirectional path tracing and Metropolis light transport based on the primary sample space. Our proposing algorithm has better convergence property than bidirectional path tracing and the Metropolis light transport, and it is easy to implement by extending the Metropolis light transport. 相似文献
27.
Joris Vaillant Abderrahmane Kheddar Hervé Audren François Keith Stanislas Brossette Adrien Escande Karim Bouyarmane Kenji Kaneko Mitsuharu Morisawa Pierre Gergondet Eiichi Yoshida Suuji Kajita Fumio Kanehiro 《Autonomous Robots》2016,40(3):561-580
We describe the research and the integration methods we developed to make the HRP-2 humanoid robot climb vertical industrial-norm ladders. We use our multi-contact planner and multi-objective closed-loop control formulated as a QP (quadratic program). First, a set of contacts to climb the ladder is planned off-line (automatically or by the user). These contacts are provided as an input for a finite state machine. The latter builds supplementary tasks that account for geometric uncertainties and specific grasps procedures to be added to the QP controller. The latter provides instant desired states in terms of joint accelerations and contact forces to be tracked by the embedded low-level motor controllers. Our trials revealed that hardware changes are necessary, and parts of software must be made more robust. Yet, we confirmed that HRP-2 has the kinematic and power capabilities to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturing factories (e.g. aircraft, shipyard) and construction sites. 相似文献
28.
Fabrication techniques of microstructures with high resolution and high aspect ratio are necessary for practical microelectromechanical
systems (MEMS) that have high performance and integration. In order to fabricate microstructures with sub-micron resolution
and high aspect ratio, deep X-ray lithography has been investigated using the compact synchrotron radiation (SR) light source
called “AURORA”. An X-ray mask for sub-micron deep X-ray lithography, which is composed of 1 μm thick Au as absorbers, 2 μm
thick SiC as a membrane and 625 μm thick Si as a frame, was designed. In preliminary experiments, the following results were
achieved: EB resist microstructures with an aspect ratio of 22 corresponding with 0.07 μm width and 1.3 μm height were formed;
a 10 μm thick PMMA resist containing no warp was formed by direct polymerization, enabling more precise gap control. 相似文献
29.
Real robots should be able to adapt autonomously to various environments in order to go on executing their tasks without breaking
down. They achieve this by learning how to abstract only useful information from a huge amount of information in the environment
while executing their tasks. This paper proposes a new architecture which performs categorical learning and behavioral learning
in parallel with task execution. We call the architectureSituation Transition Network System (STNS). In categorical learning, it makes a flexible state representation and modifies it according to the results of behaviors.
Behavioral learning is reinforcement learning on the state representation. Simulation results have shown that this architecture
is able to learn efficiently and adapt to unexpected changes of the environment autonomously.
Atsushi Ueno, Ph.D.: He is a research associate in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the
Nara Institute of Science and Technology (NAIST). He received the B.E., the M.E., and the Ph.D. degrees in aeronautics and
astronautics from the University of Tokyo in 1991, 1993, and 1997 respectively. His research interest is robot learning and
autonomous systems. He is a member of Japan Association for Artificial Intelligence (JSAI).
Hideaki Takeda, Ph.D.: He is an associate professor in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the
Nara Institute of Science and Technology (NAIST). He received his Ph.D. in precision machinery engineering from the University
of Tokyo in 1991. He has conducted research on a theory of intelligent computer-aided design systems, in particular experimental
study and logical formalization of engineering design. He is also interested in multiagent architectures and ontologies for
knowledge base systems. 相似文献
30.